TalkingData开源大规模机器学习算法库Fregata(2)

Higher speed:For Generalized Linear Model, Fregata often converges in one data epoch. For a 1 billion X 1 billion data set, Fregata can train a Generalized Linear Model in 1 minute with memory caching or 10 minutes with out it. Usually, Fregata is 10-100 times faster than MLLib.

　　Parameter Free:Fregata uses GSA SGD optimization, which dosen’t require learning rate tuning, because we found a way to calculate appropriate learning rate in the training process. When confronted with super high-dimension problem, Fregata calculates remaining memory dynamically to determine the sparseness of the output, balancing accuracy and efficiency automatically. Both features enable Fregata to be treated as a standard module in data processing for different problems.

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